{"id":"W2138441731","doi":"10.1109/ijcnn.1991.155363","title":"Neural-net method for dual subspace pattern recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Subspace topology; Hebbian theory; Computer science; Artificial neural network; Artificial intelligence; Dual (grammatical number); Pattern recognition (psychology); Set (abstract data type); Backpropagation; Net (polyhedron); Random subspace method; Layer (electronics); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005685504,0.0006266689,0.0006709843,0.0008966638,0.0003061479,0.0008485448,0.001199481,0.0007265456,0.006092631],"category_scores_gemma":[0.0009498069,0.0002509587,0.0004200279,0.0008408853,0.0003924587,0.001061036,0.0007632119,0.0009109999,0.00276033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004543232,"about_ca_system_score_gemma":0.0007677616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002584431,"about_ca_topic_score_gemma":0.002998851,"domain_scores_codex":[0.9996217,0.00006628408,0.00002466237,0.00007979869,0.0001771553,0.00003022789],"domain_scores_gemma":[0.9997087,0.00005596268,0.00002229469,0.00005955203,0.0001348829,0.00001859309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001887001,0.00007897218,0.0004770118,0.0001409845,0.00007351475,0.0001174856,0.00003215897,0.108878,0.02370583,0.02825582,0.008350126,0.8297015],"study_design_scores_gemma":[0.00001198288,0.00002265116,0.0001743166,0.000005544943,0.00001028811,0.00008834001,0.000005251695,0.9776718,0.01026367,0.005704389,0.006030019,0.00001165911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002167309,0.0001272473,0.9948655,0.00006267156,0.00007515225,0.00003029068,0.00008767213,0.001115377,0.001468805],"genre_scores_gemma":[0.07728504,0.0002105573,0.9094474,0.0001254599,0.0000551495,0.0001884878,0.0004956317,0.0001841582,0.01200804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006092631,"threshold_uncertainty_score":0.02038193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06509666865989061,"score_gpt":0.2917495880686889,"score_spread":0.2266529194087983,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}